Use batching for state classification generation

Fix race condition where custom role users are temporarily denied access after login while config is still loading. Defer route rendering in DefaultAppView until config is available so the complete role list is known before ProtectedRoute evaluates permissions

frigate/util/classification.py

@@ -499,6 +499,10 @@ def _extract_keyframes(
     """
     Extract keyframes from recordings at specified timestamps and crop to specified regions.
 
+    This implementation batches work by running multiple ffmpeg snapshot commands
+    concurrently, which significantly reduces total runtime compared to
+    processing each timestamp serially.
+
     Args:
         ffmpeg_path: Path to ffmpeg binary
         timestamps: List of timestamp dicts from _select_balanced_timestamps
@@ -508,15 +512,21 @@ def _extract_keyframes(
     Returns:
         List of paths to successfully extracted and cropped keyframe images
     """
-    keyframe_paths = []
+    from concurrent.futures import ThreadPoolExecutor, as_completed
 
-    for idx, ts_info in enumerate(timestamps):
+    if not timestamps:
+        return []
+
+    # Limit the number of concurrent ffmpeg processes so we don't overload the host.
+    max_workers = min(5, len(timestamps))
+
+    def _process_timestamp(idx: int, ts_info: dict) -> tuple[int, str | None]:
         camera = ts_info["camera"]
         timestamp = ts_info["timestamp"]
 
         if camera not in camera_crops:
             logger.warning(f"No crop coordinates for camera {camera}")
-            continue
+            return idx, None
 
         norm_x1, norm_y1, norm_x2, norm_y2 = camera_crops[camera]
 
@@ -533,7 +543,7 @@ def _extract_keyframes(
                 .get()
             )
         except Exception:
-            continue
+            return idx, None
 
         relative_time = timestamp - recording.start_time
 
@@ -547,38 +557,57 @@ def _extract_keyframes(
                 height=None,
             )
 
-            if image_data:
-                nparr = np.frombuffer(image_data, np.uint8)
-                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            if not image_data:
+                return idx, None
 
-                if img is not None:
-                    height, width = img.shape[:2]
+            nparr = np.frombuffer(image_data, np.uint8)
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
-                    x1 = int(norm_x1 * width)
-                    y1 = int(norm_y1 * height)
-                    x2 = int(norm_x2 * width)
-                    y2 = int(norm_y2 * height)
+            if img is None:
+                return idx, None
 
-                    x1_clipped = max(0, min(x1, width))
-                    y1_clipped = max(0, min(y1, height))
-                    x2_clipped = max(0, min(x2, width))
-                    y2_clipped = max(0, min(y2, height))
+            height, width = img.shape[:2]
 
-                    if x2_clipped > x1_clipped and y2_clipped > y1_clipped:
-                        cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
-                        resized = cv2.resize(cropped, (224, 224))
+            x1 = int(norm_x1 * width)
+            y1 = int(norm_y1 * height)
+            x2 = int(norm_x2 * width)
+            y2 = int(norm_y2 * height)
 
-                        output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
-                        cv2.imwrite(output_path, resized)
-                        keyframe_paths.append(output_path)
+            x1_clipped = max(0, min(x1, width))
+            y1_clipped = max(0, min(y1, height))
+            x2_clipped = max(0, min(x2, width))
+            y2_clipped = max(0, min(y2, height))
 
+            if x2_clipped <= x1_clipped or y2_clipped <= y1_clipped:
+                return idx, None
+
+            cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
+            resized = cv2.resize(cropped, (224, 224))
+
+            output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
+            cv2.imwrite(output_path, resized)
+            return idx, output_path
         except Exception as e:
             logger.debug(
                 f"Failed to extract frame from {recording.path} at {relative_time}s: {e}"
             )
-            continue
+            return idx, None
 
-    return keyframe_paths
+    keyframes_with_index: list[tuple[int, str]] = []
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        future_to_idx = {
+            executor.submit(_process_timestamp, idx, ts_info): idx
+            for idx, ts_info in enumerate(timestamps)
+        }
+
+        for future in as_completed(future_to_idx):
+            _, path = future.result()
+            if path:
+                keyframes_with_index.append((future_to_idx[future], path))
+
+    keyframes_with_index.sort(key=lambda item: item[0])
+    return [path for _, path in keyframes_with_index]
 
 
 def _select_distinct_images(

web/src/App.tsx

@@ -14,6 +14,7 @@ import ProtectedRoute from "@/components/auth/ProtectedRoute";
 import { AuthProvider } from "@/context/auth-context";
 import useSWR from "swr";
 import { FrigateConfig } from "./types/frigateConfig";
+import ActivityIndicator from "@/components/indicators/activity-indicator";
 
 const Live = lazy(() => import("@/pages/Live"));
 const Events = lazy(() => import("@/pages/Events"));
@@ -50,6 +51,13 @@ function DefaultAppView() {
   const { data: config } = useSWR<FrigateConfig>("config", {
     revalidateOnFocus: false,
   });
+
+  // Compute required roles for main routes, ensuring we have config first
+  // to prevent race condition where custom roles are temporarily unavailable
+  const mainRouteRoles = config?.auth?.roles
+    ? Object.keys(config.auth.roles)
+    : undefined;
+
   return (
     <div className="size-full overflow-hidden">
       {isDesktop && <Sidebar />}
@@ -68,13 +76,11 @@ function DefaultAppView() {
           <Routes>
             <Route
               element={
-                <ProtectedRoute
-                  requiredRoles={
-                    config?.auth.roles
-                      ? Object.keys(config.auth.roles)
-                      : ["admin", "viewer"]
-                  }
-                />
+                mainRouteRoles ? (
+                  <ProtectedRoute requiredRoles={mainRouteRoles} />
+                ) : (
+                  <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
+                )
               }
             >
               <Route index element={<Live />} />